"So Many Other Things Improve" with Transdiagnostic Treatment for Sleep and Circadian Problems: Interviews with Community Providers on Treating Clients with Serious Mental Illness.

Community mental health centers (CMHCs) offer invaluable, publicly-funded treatment for serious mental illness (SMI). Unfortunately, evidence-based psychological treatments are often not delivered at CMHCs, in part due to implementation barriers, such as limited time, high caseloads, and complex clinical presentations. Transdiagnostic treatments may help address these barriers, because they allow providers to treat symptoms across multiple disorders concurrently. However, little research has investigated CMHC providers' experiences of delivering transdiagnostic treatments "on the ground," particularly for adults with SMI. Thus, the aim of the present study was to assess CMHC providers' perspectives on delivering a transdiagnostic treatment - the Transdiagnostic Intervention for Sleep and Circadian Dysfunction (TranS-C) - to adults diagnosed with SMI. In the context of a larger parent trial, providers were randomized to deliver a standard version of TranS-C (Standard TranS-C) or a version adapted to the CMHC context (Adapted TranS-C). Twenty-five providers from the parent trial participated in a semi-structured interview (n = 10 Standard TranS-C; n = 15 from Adapted TranS-C). Responses were deductively and inductively coded to identify themes related to Proctor's taxonomy of implementation outcomes. Four novel "transdiagnostic take homes" were identified: (1) transdiagnostic targets, such as sleep, can be perceived as motivating and appropriate when treating SMI, (2) strategies to bolster client motivation/adherence and address a wider range of symptom severity may improve transdiagnostic treatments, (3) balancing feasibility with offering in-depth resources is an important challenge for transdiagnostic treatment development, and (4) adapting transdiagnostic treatments to the CMHC context may improve provider perceptions of implementation outcomes.

Use of fractals in determining the malignancy degree of lung nodules.

Introduction: A Computer-Assisted Detection (CAD) System for classification into malignant-benign classes using CT images is proposed. Methods: Two methods that use the fractal dimension (FD) as a measure of the lung nodule contour irregularities (Box counting and Power spectrum) were implemented. The LIDC-IDRI database was used for this study. Of these, 100 slices belonging to 100 patients were analyzed with both methods. Results: The performance between both methods was similar with an accuracy higher than 90%. Little overlap was obtained between FD ranges for the different malignancy grades with both methods, being slightly better in Power spectrum. Box counting had one more false positive than Power spectrum. Discussion: Both methods are able to establish a boundary between the high and low malignancy degree. To further validate these results and enhance the performance of the CAD system, additional studies will be necessary.

The Transdiagnostic Intervention for Sleep and Circadian Dysfunction (TranS-C) for serious mental illness in community mental health part 3: study protocol to evaluate sustainment in a hybrid type 2 effectiveness-implementation cluster-randomized trial.

BACKGROUND: Although research on the implementation of evidence-based psychological treatments (EBPTs) has advanced rapidly, research on the sustainment of implemented EBPTs remains limited. This is concerning, given that EBPT activities and benefits regularly decline post-implementation. To advance research on sustainment, the present protocol focuses on the third and final phase-the Sustainment Phase-of a hybrid type 2 cluster-randomized controlled trial investigating the implementation and sustainment of the Transdiagnostic Intervention for Sleep and Circadian Dysfunction (TranS-C) for patients with serious mental illness and sleep and circadian problems in community mental health centers (CMHCs). Prior to the first two phases of the trial-the Implementation Phase and Train-the-Trainer Phase-TranS-C was adapted to fit the CMHC context. Then, 10 CMHCs were cluster-randomized to implement Standard or Adapted TranS-C via facilitation and train-the-trainer. The primary goal of the Sustainment Phase is to investigate whether adapting TranS-C to fit the CMHC context predicts improved sustainment outcomes. METHODS: Data collection for the Sustainment Phase will commence at least three months after implementation efforts in partnering CMHCs have ended and may continue for up to one year. CMHC providers will be recruited to complete surveys (N = 154) and a semi-structured interview (N = 40) on sustainment outcomes and mechanisms. Aim 1 is to report the sustainment outcomes of TranS-C. Aim 2 is to evaluate whether manipulating EBPT fit to context (i.e., Standard versus Adapted TranS-C) predicts sustainment outcomes. Aim 3 is to test whether provider perceptions of fit mediate the relation between treatment condition (i.e., Standard versus Adapted TranS-C) and sustainment outcomes. Mixed methods will be used to analyze the data. DISCUSSION: The present study seeks to advance our understanding of sustainment predictors, mechanisms, and outcomes by investigating (a) whether the implementation strategy of adapting an EBPT (i.e., TranS-C) to the CMHC context predicts improved sustainment outcomes and (b) whether this relation is mediated by improved provider perceptions of treatment fit. Together, the findings may help inform more precise implementation efforts that contribute to lasting change. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT05956678 . Registered on July 21, 2023.

The Transdiagnostic Intervention for Sleep and Circadian Dysfunction (TranS-C) for serious mental illness in community mental health part 1: study protocol for a hybrid type 2 effectiveness-implementation cluster-randomized trial.

BACKGROUND: Serious mental illness (SMI) can have devastating consequences. Unfortunately, many patients with SMI do not receive evidence-based psychological treatment (EBPTs) in routine practice settings. One barrier is poor "fit" between EBPTs and contexts in which they are implemented. The present study will evaluate implementation and effectiveness outcomes of the Transdiagnostic Intervention for Sleep and Circadian Dysfunction (TranS-C) implemented in community mental health centers (CMHCs). TranS-C was designed to target a range of SMI diagnoses by addressing a probable mechanism and predictor of SMI: sleep and circadian problems. We will investigate whether adapting TranS-C to fit CMHC contexts improves providers' perceptions of fit and patient outcomes. METHODS: TranS-C will be implemented in at least ten counties in California, USA (N = 96 providers; N = 576 clients), via facilitation. CMHC sites are cluster-randomized by county to Adapted TranS-C or Standard TranS-C. Within each county, patients are randomized to immediate TranS-C or usual care followed by delayed treatment with TranS-C (UC-DT). Aim 1 will compare TranS-C (combined Adapted and Standard) with UC-DT on improvements in sleep and circadian problems, functional impairment, and psychiatric symptoms. Sleep and circadian problems will also be tested as a mediator between treatment condition (combined TranS-C versus UC-DT) and functional impairment/psychiatric symptoms. Aim 2 will evaluate whether Adapted TranS-C is superior to Standard TranS-C with respect to provider perceptions of fit. Aim 3 will evaluate whether the relation between TranS-C treatment condition (Adapted versus Standard) and patient outcomes is mediated by better provider perceptions of fit in the Adapted condition. Exploratory analyses will (1) compare Adapted versus Standard TranS-C on patient perceptions of credibility/improvement and select PhenX Toolkit outcomes and (2) evaluate possible moderators. DISCUSSION: This trial has the potential to (a) expand support for TranS-C, a promising transdiagnostic treatment delivered to patients with SMI in CMHCs; (b) take steps toward addressing challenges faced by providers in delivering EBPTs (i.e., high caseloads, complex patients, poor fit); and (c) advance evidence on causal strategies (i.e., adapting treatments to fit context) in implementation science. TRIAL REGISTRATION: Clinicaltrials.gov NCT04154631. Registered on 6 November 2019. https://clinicaltrials.gov/ct2/show/NCT04154631.

The Transdiagnostic Intervention for Sleep and Circadian Dysfunction (TranS-C) for serious mental illness in community mental health part 3: Study protocol to evaluate sustainment in a hybrid type 2 effectiveness-implementation cluster-randomized trial.

treatments (EBPTs) has advanced rapidly, research on the sustainment of implemented EBPTs remains limited. This is concerning, given that EBPT activities and benefits regularly decline post-implementation. To advance research on sustainment, the present protocol focuses on the third and final phase - the Sustainment Phase - of a hybrid type 2 cluster-randomized controlled trial investigating the implementation and sustainment of the Transdiagnostic Intervention for Sleep and Circadian Dysfunction (TranS-C) for patients with serious mental illness and sleep and circadian problems in community mental health centers (CMHCs). Prior to the first two phases of the trial - the Implementation Phase and Train-the-Trainer Phase - TranS-C was adapted to fit the CMHC context. Then, 10 CMHCs were cluster-randomized to implement Standard or Adapted TranS-C via facilitation and train-the-trainer. The primary goal of the Sustainment Phase is to investigate whether adapting TranS-C to fit the CMHC context predicts improved sustainment outcomes. Methods: Data collection for the Sustainment Phase will commence at least three months after implementation efforts in partnering CMHCs have ended and may continue for up to one year. CMHC providers will be recruited to complete surveys (N = 154) and a semi-structured interview (N = 40) on sustainment outcomes and mechanisms. Aim 1 is to report the sustainment outcomes of TranS-C. Aim 2 is to evaluate whether manipulating EBPT fit to context (i.e., Standard versus Adapted TranS-C) predicts sustainment outcomes. Aim 3 is to test whether provider perceptions of fit mediate the relation between treatment condition (i.e., Standard versus Adapted TranS-C) and sustainment outcomes. Mixed methods will be used to analyze the data. Discussion: The present study seeks to advance our understanding of sustainment predictors, mechanisms, and outcomes by investigating (a) whether the implementation strategy of adapting an EBPT (i.e., TranS-C) to the CMHC context predicts improved sustainment outcomes and (b) whether this relation is mediated by improved provider perceptions of treatment fit. Together, the findings may help inform more precise implementation efforts that contribute to lasting change. Trial Registration: ClinicalTrials.gov identifier: NCT05956678. Registered on July 21, 2023. https://classic.clinicaltrials.gov/ct2/show/NCT05956678?term=NCT05956678&draw=2&rank=1.

The Transdiagnostic Intervention for Sleep and Circadian Dysfunction (TranS-C) for serious mental illness in community mental health part 2: study protocol for a hybrid type 2 effectiveness-implementation cluster-randomized trial using train-the-trainer.

BACKGROUND: Train-the-trainer (TTT) is a promising method for implementing evidence-based psychological treatments (EBPTs) in community mental health centers (CMHCs). In TTT, expert trainers train locally embedded individuals (i.e., Generation 1 providers) to deliver an EBPT, who then train others (i.e., Generation 2 providers). The present study will evaluate implementation and effectiveness outcomes of an EBPT for sleep and circadian dysfunction-the Transdiagnostic Intervention for Sleep and Circadian Dysfunction (TranS-C)-delivered to CMHC patients with serious mental illness by Generation 2 providers (i.e., trained and supervised within CMHCs via TTT). Specifically, we will investigate whether adapting TranS-C to fit CMHC contexts improves Generation 2 (a) patient outcomes and (b) providers' perceptions of fit. METHODS: TTT will be implemented in nine CMHCs in California, USA (N = 60 providers; N = 130 patients) via facilitation. CMHCs are cluster-randomized by county to Adapted TranS-C or Standard TranS-C. Within each CMHC, patients are randomized to immediate TranS-C or usual care followed by delayed treatment with TranS-C (UC-DT). Aim 1 will assess the effectiveness of TranS-C (combined Adapted and Standard), compared to UC-DT, on improvements in sleep and circadian problems, functional impairment, and psychiatric symptoms for Generation 2 patients. Aim 2 will evaluate whether Adapted TranS-C is superior to Standard TranS-C with respect to Generation 2 providers' perceptions of fit. Aim 3 will evaluate whether Generation 2 providers' perceived fit mediates the relation between TranS-C treatment condition and patient outcomes. Exploratory analyses will (1) evaluate whether the effectiveness of TranS-C for patient outcomes is moderated by generation, (2) compare Adapted and Standard TranS-C on patient perceptions of credibility/improvement and PhenX Toolkit outcomes (e.g., substance use, suicidality), and (3) evaluate other possible moderators. DISCUSSION: This trial has potential to (a) inform the process of embedding local trainers and supervisors to expand delivery of a promising transdiagnostic treatment for sleep and circadian dysfunction, (b) add to the growing body of TTT literature by evaluating TTT outcomes with a novel treatment and population, and (c) advance our understanding of providers' perceptions of EBPT "fit" across TTT generations. TRIAL REGISTRATION: ClinicalTrials.gov identifier NCT05805657 . Registered on April 10, 2023.

The Transdiagnostic Intervention for Sleep and Circadian Dysfunction (TranS-C) for serious mental illness in community mental health part 2: Study protocol for a hybrid type 2 effectiveness-implementation cluster- randomized trial using train-the-trainer.

Background: Train-the-trainer (TTT) is a promising method for implementing evidence-based psychological treatments (EBPTs) in community mental health centers (CMHCs). In TTT, expert trainers train locally embedded individuals (i.e., Generation 1 providers) to deliver an EBPT, who then train others (i.e., Generation 2 providers). The present study will evaluate implementation and effectiveness outcomes of an EBPT for sleep and circadian dysfunction-the Transdiagnostic Intervention for Sleep and Circadian Dysfunction (TranS-C)-delivered to CMHC patients with serious mental illness by Generation 2 providers (i.e., trained and supervised within CMHCs via TTT). Specifically, we will investigate whether adapting TranS-C to fit CMHC contexts improves Generation 2 (a) patient outcomes (b) providers' perceptions of fit. Methods: TTT will be implemented in nine CMHCs in California, United States (N= 60 providers; N= 130 patients) via facilitation. CMHCs are cluster-randomized by county to Adapted TranS-C or Standard TranS-C. Within each CMHC, patients are randomized to immediate TranS-C or usual care followed by delayed treatment with TranS-C (UC-DT). Aim 1 will assess the effectiveness of TranS-C (combined Adapted and Standard), compared to UC-DT, on improvements in sleep and circadian problems, functional impairment, and psychiatric symptoms for Generation 2 patients. Aim 2 will evaluate whether Adapted TranS-C is superior to Standard TranS-C with respect to Generation 2 providers' perceptions of fit. Aim 3 will evaluate whether Generation 2 providers' perceived fit mediates the relation between TranS-C treatment condition and patient outcomes. Exploratory analyses will: (1) evaluate whether the effectiveness of TranS-C for patient outcomes is moderated by generation, (2) compare Adapted and Standard TranS-C on patient perceptions of credibility/improvement and PhenX Toolkit outcomes (e.g., substance use, suicidality); and (3) evaluate other possible moderators. Discussion: This trial has potential to inform the process of (a) embedding local trainers and supervisors to expand delivery of a promising transdiagnostic treatment for sleep and circadian dysfunction, (b) adding to the growing body of TTT literature by evaluating TTT outcomes with a novel treatment and population, and (c) advancing our understanding of providers' perceptions of EBPT 'fit' across TTT generations. Trial registration: Clinicaltrials.gov identifier: NCT05805657. Registered on April 10, 2023. https://clinicaltrials.gov/ct2/show/NCT05805657.

New patch-based strategy for COVID-19 automatic identification using chest x-ray images.

Purpose: The development of a robust model for automatic identification of COVID-19 based on chest x-rays has been a widely addressed topic over the last couple of years; however, the scarcity of good quality images sets, and their limited size, have proven to be an important obstacle to obtain reliable models. In fact, models proposed so far have suffered from over-fitting erroneous features instead of learning lung features, a phenomenon known as shortcut learning. In this research, a new image classification methodology is proposed that attempts to mitigate this problem. Methods: To this end, annotation by expert radiologists of a set of images was performed. The lung region was then segmented and a new classification strategy based on a patch partitioning that improves the resolution of the convolution neural network is proposed. In addition, a set of native images, used as an external evaluation set, is released. Results: The best results were obtained for the 6-patch splitting variant with 0.887 accuracy, 0.85 recall and 0.848 F1score on the external validation set. Conclusion: The results show that the proposed new strategy maintains similar values between internal and external validation, which gives our model generalization power, making it available for use in hospital settings. Supplementary Information: The online version contains supplementary material available at 10.1007/s12553-022-00704-4.

Can integrating the Memory Support Intervention into cognitive therapy improve depression outcome? A randomized controlled trial.

We investigated if improving a patient's memory for the content of their treatment, via the Memory Support Intervention, improves illness course and functional outcomes. The platform for investigating this question was major depressive disorder (MDD) and cognitive therapy (CT). Adults diagnosed with MDD (N = 178) were randomly allocated to CT + Memory Support (n = 91) or CT-as-usual (n = 87). Both treatments were comprised of 20-26, 50-min sessions over 16 weeks. Blind assessments were conducted before and immediately following treatment (post-treatment) and 6 months later (6FU). Patient memory for treatment, assessed with a free recall task, was higher in CT + Memory Support for past session recall at post-treatment. Both treatment arms were associated with reductions in depressive symptoms and functional impairment except: CT + Memory Support exhibited lower depression severity at 6FU (b = -3.09, p = 0.050, d = -0.27), and greater reduction in unhealthy days from baseline to 6FU (b = -4.21, p = 0.010, d = -1.07), compared to CT-as-usual. While differences in illness course and functional outcomes between the two treatment arms were limited, it is possible that future analyses of the type of memory supports and longer follow-up may yield more encouraging outcomes. TRIAL REGISTRATION: ClinicalTrials.gov NCT01790919. Registered October 6, 2016.

Inpainting-filtering for metal artifact reduction (IMIF-MAR) in computed tomography.

The reduction of metal artifacts remains a challenge in computed tomography because they decrease image quality, and consequently might affect the medical diagnosis. The objective of this study is to present a novel method to correct metal artifacts based solely on the CT-slices. The proposed method consists of four steps. First, metal implants in the original CT-slice are segmented using an entropy based method, producing a metal image. Second, a prior image is acquired using three transformations: Gaussian filter, Parisotto and Schoenlieb inpainting method with the Mumford-Shah image model and L0 Gradient Minimization method (L0GM). Next, based on the projections from the original CT-slice, prior image and metal image, the sinogram is corrected in the traces affected by metal in the process called normalization and denormalization. Finally, the reconstructed image is obtained by FBP and a Nonlocal Means (NLM) filtering. The efficacy of the algorithm is evaluated by comparing five image quality metrics of the images and by inspecting regions of interest (ROI). Phantom data as well as clinical datasets are included. The proposed method is compared with three established metal artifact reduction (MAR) methods. The results from a phantom and clinical dataset show the visible reduction of artifacts. The conclusion is that IMIF-MAR method can reduce streak metal artifacts effectively and avoid new artifacts around metal implants, while preserving the anatomical structures. Considering both clinical and phantom studies, the proposed MAR algorithm improves the quality of clinical images affected by metal artifacts, and could be integrated in clinical setting.

Current limitations to identify COVID-19 using artificial intelligence with chest X-ray imaging.

The scientific community has joined forces to mitigate the scope of the current COVID-19 pandemic. The early identification of the disease, as well as the evaluation of its evolution is a primary task for the timely application of medical protocols. The use of medical images of the chest provides valuable information to specialists. Specifically, chest X-ray images have been the focus of many investigations that apply artificial intelligence techniques for the automatic classification of this disease. The results achieved to date on the subject are promising. However, some results of these investigations contain errors that must be corrected to obtain appropriate models for clinical use. This research discusses some of the problems found in the current scientific literature on the application of artificial intelligence techniques in the automatic classification of COVID-19. It is evident that in most of the reviewed works an incorrect evaluation protocol is applied, which leads to overestimating the results.

Current limitations to identify covid-19 using artificial intelligence with chest x-ray imaging (part ii). The shortcut learning problem.

Since the outbreak of the COVID-19 pandemic, computer vision researchers have been working on automatic identification of this disease using radiological images. The results achieved by automatic classification methods far exceed those of human specialists, with sensitivity as high as 100% being reported. However, prestigious radiology societies have stated that the use of this type of imaging alone is not recommended as a diagnostic method. According to some experts the patterns presented in these images are unspecific and subtle, overlapping with other viral pneumonias. This report seeks to evaluate the analysis the robustness and generalizability of different approaches using artificial intelligence, deep learning and computer vision to identify COVID-19 using chest X-rays images. We also seek to alert researchers and reviewers to the issue of "shortcut learning". Recommendations are presented to identify whether COVID-19 automatic classification models are being affected by shortcut learning. Firstly, papers using explainable artificial intelligence methods are reviewed. The results of applying external validation sets are evaluated to determine the generalizability of these methods. Finally, studies that apply traditional computer vision methods to perform the same task are considered. It is evident that using the whole chest X-Ray image or the bounding box of the lungs, the image regions that contribute most to the classification appear outside of the lung region, something that is not likely possible. In addition, although the investigations that evaluated their models on data sets external to the training set, the effectiveness of these models decreased significantly, it may provide a more realistic representation as how the model will perform in the clinic. The results indicate that, so far, the existing models often involve shortcut learning, which makes their use less appropriate in the clinical setting.

Segmentación del hígado en imágenes de tomografía computarizada

RESUMEN Fundamento: la segmentación del hígado utilizando datos de tomografía computarizada es el primer paso para el diagnóstico de enfermedades hepáticas. Actualmente la segmentación de estructuras y órganos, basado en imágenes, que se realiza en los hospitales del país, dista de tener los niveles de precisión que se obtienen de los modernos sistemas 3D, por lo que se requiere buscar alternativas viables utilizando el PDI sobre ordenador. Objetivo: determinar una variante eficaz y eficiente desde el punto de vista computacional en condiciones de rutina hospitalaria, para la segmentación de imágenes hepáticas con fines clínicos. Métodos: se compararon dos métodos modernos de segmentación (Graph Cut y EM/MPM) aplicándolos sobre imágenes de tomografía de hígado. Se realizó un análisis evaluativo y estadístico de los resultados obtenidos en la segmentación de las imágenes a partir de los coeficientes de Dice, Vinet y Jaccard. Resultados: con el método Graph Cut, en todos los casos, se segmentó la región deseada, incluso cuando la calidad de las imágenes era baja, se observó gran similitud entre la imagen segmentada y la máscara de referencia. El nivel de detalles visuales es bueno y la reproducción de bordes permanece fiel a la máscara de referencia. La segmentación de las imágenes por el método de EM/MPM, no siempre fue satisfactoria. Conclusiones: el método de segmentación Graph Cut obtuvo mayor precisión para segmentar imágenes de hígado.

ABSTRACT Background: liver segmentation using computed tomography data is the first step for the diagnosis of liver diseases. Currently, the segmentation of structures and organs, based on images, which is carried out in the country's hospitals, is far from having the levels of precision obtained from modern 3D systems, it is necessary to search for viable alternatives using the PDI on a computer. Objective: to determine an effective and efficient variant from the computational point of view in routine hospital conditions, for the segmentation of liver images for clinical purposes. Methods: Two modern segmentation methods (Graph Cut and EM/MPM) were compared by applying them to liver tomography images. An evaluative and statistical analysis of the results obtained in the segmentation of the images from the Dice, Vinet and Jaccard coefficients was carried out. Results: with the Graph Cut method, in all cases, the desired region was segmented, even when the quality of the images was low, great similarity was observed between the segmented image and the reference mask. The level of visual detail is good, and edge reproduction remains true to the reference skin. Image segmentation by the EM/MPM method was not always satisfactory. Conclusions: the Graph Cut segmentation method obtained greater precision to segment liver images.

Inteligencia artificial explicable, una perspectiva al problema de la clasificación automática de COVID-19 mediante radiografías de tórax

RESUMEN Esta investigación pretende dilucidar, a partir del análisis de técnicas de inteligencia artificial explicables, la robustez y el nivel de generalización de los métodos de visión por computadora propuestos para identificar COVID-19 utilizando imágenes de radiografías de tórax. Asimismo, alertar a los investigadores y revisores sobre el problema del aprendizaje por atajos. En este estudio se siguen recomendaciones para identificar si los modelos de clasificación automática de COVID-19 se ven afectados por el aprendizaje por atajos. Para ello, se revisaron los artículos que utilizan métodos de inteligencia artificial explicable en dicha tarea. Se evidenció que al utilizar la imagen de radiografía de tórax completa o el cuadro delimitador de los pulmones, las regiones de la imagen que más contribuyen a la clasificación aparecen fuera de la región pulmonar, algo que no tiene sentido. Los resultados indican que, hasta ahora, los modelos existentes presentan el problema de aprendizaje por atajos, lo cual los hace inapropiados para ser usados en entornos clínicos.

ABSTRACT This research aims to elucidate, from the analysis of explainable artificial intelligence techniques, the robustness and level of generalization of the proposed computer vision methods to identify COVID-19 using chest X-ray images. Also, alert researchers and reviewers about the problem of learning by shortcuts. In this study, recommendations are followed to identify if the automatic classification models of COVID-19 are affected by shortcut learning. To do this, articles that use explainable artificial intelligence methods were reviewed. It was shown that when using the full chest X-ray image or the bounding box of the lungs, the regions of the image that contribute the most to the classification appear outside the lung region, something that does not make sense. The results indicate that, so far, the existing models present the problem of learning by shortcuts, which makes them inappropriate to be used in clinical settings.

Activity optimization method in SPECT: a comparison with ROC analysis.

A discriminant method for optimizing activity in nuclear medicine studies is validated by comparison with ROC (received operating characteristic)-curves. The method is tested in 21 single photon emission computerized tomography (SPECT), performed with a cardiac phantom. Three different lesions (L(1), L(2) and L(3)) were placed in the myocardium-wall by pairs for each SPECT. Three activities (84, 37 or 18.5 MBq) of 99mTc were used as background. Linear discriminant analysis was used to select the parameters that characterize image quality among the measured variables in the images [(Background-to-Lesion (B/L(i)) and Signal-to-Noise (S(i)/N) ratios)]. Two clusters with different image quality (P=0.021) were obtained. The ratios B/L(1), B/L(2) and B/L(3) are the parameters used to construct the function with 100% of cases correctly classified into the clusters. The value of 37 MBq was the lowest tested activity for which good results for the B/L(i) ratios were obtained. The result coincides with the applied ROC-analysis (r=0.89).

Inteligencia artificial: una herramienta en la imagenología para los pacientes positivos a la COVID-19 / Artificial intelligence: an imaging tool for COVID-19 positive patients

RESUMEN Introducción: la enfermedad por SARS-Cov-2 refuerza la importancia del uso de las nuevas tecnologías de la información y las comunicaciones en función del desarrollo e implementación de sistemas de inteligencia artificial que favorecen el diagnóstico. Objetivo: describir la posibilidad del uso de la inteligencia artificial como una herramienta en la imagenología para los pacientes positivos a la COVID-19. Métodos: se realizó una revisión de fuentes bibliográficas en Infomed, SciELO, PubMed y Google Académico, comprendidas en los años 2015 al 2020 con el uso de palabras claves: coronavirus, COVID-19, neumonía, radiografía e inteligencia artificial. Se seleccionaron 28 documentos por su pertinencia en el estudio. Desarrollo: la creación de sistemas de inteligencia artificial que ayuden al diagnóstico médico requiere un enfoque interprofesional de la ciencia y constituye una de las líneas de trabajo en Cuba durante la pandemia. Una condición indispensable para la introducción de la inteligencia artificial en el diagnóstico radiológico es la capacitación que deben recibir los médicos para interactuar con ella, a través de un proceso formativo que incluya una evaluación y explicación de la calidad de los datos asociada tanto al aprendizaje como a las nuevas predicciones. Conclusiones: la utilización de inteligencia artificial mejorará el rendimiento del radiólogo para distinguir la COVID-19; la integración de estas tecnologías en el flujo de trabajo clínico de rutina puede ayudar a los radiólogos a diagnosticar con precisión.

ABSTRACT Introduction: SARS-Cov-2 disease reinforces the importance of the use of new information and communication technologies based on the development and implementation of artificial intelligence systems that favor diagnosis. Objective: to describe the possibility of using artificial intelligence as a tool in imaging for COVID-19 positive patients. Methods: a review of bibliographic sources was carried out in Infomed, SciELO, PubMed and Google Scholar, from 2015 to 2020 with the use of keywords: coronavirus, COVID-19, pneumonia, radiography and artificial intelligence. 28 documents were selected for their relevance in the study. Development: the creation of artificial intelligence systems that help medical diagnosis requires an interprofessional approach to science and constitutes one of the lines of work in Cuba during the pandemic. An essential condition for the introduction of artificial intelligence in radiological diagnosis is the training that doctors must receive to interact with it, through a training process that includes an evaluation and explanation of the quality of the data associated with both learning and to new predictions. Conclusions: the use of artificial intelligence will improve the radiologist's performance to distinguish COVID-19; integrating these technologies into routine clinical workflow can help radiologists diagnose accurately.

Caracterización clínica y radiológica de pacientes positivos a la COVID-19 / Clinical and radiological characterization of COVID-19 positive patients

Introducción: Desde el surgimiento de los primeros casos en la pandemia de la COVID-19, se ha desarrollado una carrera vertiginosa en crear un espacio de investigación para el diagnóstico, tratamiento y control de la enfermedad. Objetivo: Describir las características clínicas y radiológicas de los pacientes con la COVID-19. Métodos: Se realizó un estudio descriptivo, en el período comprendido de marzo a octubre del año 2020, se estudiaron 404 pacientes de todas las edades, ingresados, con diagnóstico confirmado con PCR en tiempo real. Las variables utilizadas fueron: edad, sexo, síntomas y radiografía del tórax. Resultados: El 54,5 por ciento de los pacientes fueron del sexo femenino y entre ellos asintomáticos el 55,9 por ciento; el 36,9 por ciento tenía entre 40 a 59 años de edad, en los menores de 20 años, el 64,9 por ciento no presentó síntomas de la enfermedad al ingreso. Estuvieron asintomáticos el 53,5 por ciento; el 76,6 por ciento de las radiografías positivas correspondieron a los sintomáticos, la tos fue el síntoma más frecuente. La mayor positividad en la radiografía del tórax se encontró en los pacientes mayores de 60 años, se observó como patrón más frecuente, la opacidad en velo, de distribución periférica. Conclusiones: Predominan los pacientes asintomáticos, la positividad de las radiografías es mayor en los ancianos(AU)

Introduction: Since the emergence of the first cases of COVID-19 pandemic, a dizzying race has developed in creating a research space for the diagnosis, treatment and control of the disease. Objective: To describe the clinical and radiological characteristics of patients with COVID-19. Methods: A descriptive study was carried out, in the period from March to October 2020, 404 patients of all ages, admitted, with confirmed diagnosis with real-time PCR, were studied. The variables used were: age, sex, symptoms and chest X-ray. Results: 54.5 percent of the patients were female and 55,9 percent of them were asymptomatic, 36,9 percent were between 40 and 59 years old, in those under 20 years 64,9 percent were not. They presented symptoms of the disease upon admission 53,5 percent were asymptomatic, 76,6 percent of the positive radiographs corresponded to the symptomatic ones, coughing was the most frequent symptom. The greatest positivity in the chest X-ray was found in patients older than 60 years, the most frequent pattern was the opacity in the peripheral distribution veil. Conclusions: Asymptomatic patients predominate, the positivity of radiographs is higher in the elderly(AU)

Administered activity optimization in 99mTc-MAG3 renography for adults.

OBJECTIVE: The objective of this work was to determine the minimum administered activity of (99m)Tc-mercaptoacetyltriglycine (MAG3) needed both to estimate effective renal plasma flow (ERPF) with adequate precision and to obtain good image quality. METHODS: Three groups of 10 patients each were injected with 45, 71, or 132 MBq of MAG3. Renograms and perfusion and clearance images were obtained. The age, sex, and weight of the patients; the labeling yield; the mean count and counting rate 2 min after injection; the kidney-to-background and cortex-to-background ratios; the uptake time from the renograms; the percentage of the injected activity 2 min after injection in the left and right kidneys (A2(LK) and A2(RK), respectively); and the ERPF for both kidneys were obtained and analyzed. Discriminant analysis of image quality was used to select the variables that most affected image quality. The selected variables were studied among activity groups to optimize the amount of activity administered in these studies. RESULTS: Precision in ERPF assessment did not significantly differ among administered activity levels (P = 0.824). The SDs of the ERPF were +/-1.5 for 132 MBq, +/-1.7 for 71 MBq, and +/-2.0 for 45 MBq. The labeling yield, the ratios of counts in the left and right kidneys to the background and in the left and right cortices to the background, and A2(LK) and A2(RK) were the only variables that provided a significant discriminant function for image quality. The only variable that significantly differed with the variation in administered activity was the ratio of counts in the right kidney to the background (P = 0.026), most likely because of the labeling yield. CONCLUSION: A 45-MBq activity is sufficient to guarantee good image quality and adequate precision in ERPF determination from the time-activity curve, provided the labeling yield is kept high.